Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Genetic programming II: automatic discovery of reusable programs
Genetic programming II: automatic discovery of reusable programs
Foundations of genetic programming
Foundations of genetic programming
Option Valuation With Generalized Ant Programming
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
Lexicographic Parsimony Pressure
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
Introduction to Evolutionary Computing
Introduction to Evolutionary Computing
Proceedings of the 35th conference on Winter simulation: driving innovation
Biologically Inspired Algorithms for Financial Modelling (Natural Computing Series)
Biologically Inspired Algorithms for Financial Modelling (Natural Computing Series)
Option pricing using Particle Swarm Optimization
C3S2E '09 Proceedings of the 2nd Canadian Conference on Computer Science and Software Engineering
Ant colony optimization to price exotic options
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Particle swarm optimization algorithm for option pricing: extended abstract
Proceedings of the 12th annual conference companion on Genetic and evolutionary computation
The Journal of Supercomputing
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Genetic Programming (GP) is an automated computational programming methodology, inspired by the workings of natural evolution techniques. It has been applied to solve complex problems in multiple domains including finance. This paper illustrates the application of an adaptive form of GP, where the probability of crossover and mutation is adapted dynamically during the GP run, to the important real-world problem of options pricing. The tests are carried out using market option price data and the results illustrate that the new method yields better results than are obtained from GP with fixed crossover and mutation rates. The developed method has potential for implementation across a range of dynamic problem environments.